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Stream-based active learning for sliding windows under the influence of verification latency

, , , and . Machine Learning, 111 (6): 2011--2036 (2022)
DOI: doi.org/10.1007/s10994-021-06099-z

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Online Clustering of High-Dimensional Trajectories under Concept Drift., , and . ECML/PKDD (2), volume 6912 of Lecture Notes in Computer Science, page 261-276. Springer, (2011)Clustering-Based Optimised Probabilistic Active Learning (COPAL)., , and . Discovery Science, volume 9356 of Lecture Notes in Computer Science, page 101-115. Springer, (2015)Temporal Aspects of Stream Active Learning, , and . (2016)Toward optimal probabilistic active learning using a Bayesian approach, , , , , and . Machine Learning, 110 (6): 1199--1231 (2021)Implementation of and experimental software for active selection of classification features., , and . Softw. Impacts, (2021)Stream-based active learning for sliding windows under the influence of verification latency, , , and . Machine Learning, 111 (6): 2011--2036 (2022)Temporal density extrapolation using a dynamic basis approach., , and . Data Min. Knowl. Discov., 33 (5): 1323-1356 (2019)Partitioner Trees for Classification: A New Ensemble Method., and . Applications of Supervised and Unsupervised Ensemble Methods, volume 245 of Studies in Computational Intelligence, Springer, (2009)Learning from monitoring unlabelled data under large verification latency, and . (2016)Advanced Topics on Data Stream Mining: Part II. Mining Multiple Streams, , and . Tutorial at the 23rd Europ. Conf. on Machine Learning and16th Europ. Conf. on Principles and Practice ofKnowledge Discovery in Databases (ECML PKDD\'12), (24-28 09 2012)